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RGB-D Salient Object Detection

RGB-D Salient object detection (SOD) aims at distinguishing the most visually distinctive objects or regions in a scene from the given RGB and Depth data. It has a wide range of applications, including video/image segmentation, object recognition, visual tracking, foreground maps evaluation, image retrieval, content-aware image editing, information discovery, photosynthesis, and weakly supervised semantic segmentation. Here, depth information plays an important complementary role in finding salient objects. Online benchmark: http://dpfan.net/d3netbenchmark.

( Image credit: Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks, TNNLS20 )

Papers

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Showing 31–40 of 88 papers

TitleStatusHype
Progressively Guided Alternate Refinement Network for RGB-D Salient Object DetectionCode1
RGB-D Salient Object Detection via 3D Convolutional Neural NetworksCode1
A Single Stream Network for Robust and Real-time RGB-D Salient Object DetectionCode1
Hierarchical Dynamic Filtering Network for RGB-D Salient Object DetectionCode1
A2dele: Adaptive and Attentive Depth Distiller for Efficient RGB-D Salient Object DetectionCode1
Learning Selective Self-Mutual Attention for RGB-D Saliency DetectionCode1
CoLA: Conditional Dropout and Language-driven Robust Dual-modal Salient Object DetectionCode1
MambaSOD: Dual Mamba-Driven Cross-Modal Fusion Network for RGB-D Salient Object DetectionCode1
Bifurcated backbone strategy for RGB-D salient object detectionCode1
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object DetectionCode1
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